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Challenges of Non-functional Requirements Extraction in Agile Software Development using Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 43 |
| Year of Publication: 2021 |
| Authors: Hoda Mohamed Abd El Sameaa, Nesrine Ali Abd El Azim, Nagy Ramadan |
10.5120/ijca2021921835
|
Hoda Mohamed Abd El Sameaa, Nesrine Ali Abd El Azim, Nagy Ramadan . Challenges of Non-functional Requirements Extraction in Agile Software Development using Machine Learning. International Journal of Computer Applications. 183, 43 ( Dec 2021), 23-26. DOI=10.5120/ijca2021921835
Abstract
Current developments in Requirement Engineering methods have seen mutation resulting from the use of machine learning algorithms to resolve several complex Requirements Engineering problems. One of these problems is the identification and classification of non-functional requirements in the requirements documents. Machine based-learning techniques for this challenge have been shown hopeful outcomes than traditional natural language processing approaches. However, there is still lacking of a systematic understanding these machine learning approaches. Despite the fact that non-functional requirements are critical to a software project's success, there is still no accords about what they are and how we will elicit, document, and validate them. Thus, the important task of Requirements Engineering is to properly extract non-functional requirements records from requirement files and arrange them into categories. However, this task is waste of time and prone to errors. This paper presents non-functional requirements importance, relates them to the process of software development and identifies its challenges and current area of research.
References
- A. M. Davis. Software requirements: objects, functions, and states. Prentice-Hall, Inc., 1993.
- Handa, N., Sharma, D. A., & Gupta, D. A. (2019). Non Functional Requirements Analysis using Data Analytics. International Journal of Advanced Science and Technology, 27, 383 - 393.
- Binkhonain, Manal, and Liping Zhao."A review of machine learning algorithms for identification and classification of non-functional requirements." Expert Systems with Applications: X 1 (2019): 100001.
- Kurtanovi ´c, Z., &Maalej, W. (2017, September).Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning. Paper presented at IEEE 25th International Requirements Engineering Conference Workshops, Lisbon, Portugal.
- NupurChugh and AdityaDev Mishra, “Assimilation of Four Layered Approach to NFR in Agile Requirement Engineering”, International Journal of Computer Applications (0975 – 8887) Volume 78 – No.5, September 2013.
- https://searchenterpriseai.techtarget.com/definition/machine-learning-ML.
- Sillitti, Alberto, and Giancarlo Succi."Requirements engineering for agile methods."Engineering and Managing Software Requirements. Springer, Berlin, Heidelberg, 2005.309-326.
- Kurtanovi´c, Z.; Maalej, W. Automatically classifying functional and non-functional requirements using supervised machine learning. In Proceedings of the 2017 IEEE 25th International Requirements Engineering Conference (RE), Lisbon, Portugal, 4–8 September 2017; pp. 490–495.
- W. Maalej and H. Nabil. 2015. Bug report, feature request, orsimply praise? On automatically classifying app reviews. In IEEE23rd International Requirements Engineering Conference (RE).
- Deocadez, R., Harrison, R., & Rodriguez, D. (2017, June). Preliminary Study on Applying Semi-Supervised Learning to App Store Analysis.In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (pp. 320–323).
- P. Singh, D. Singh and A. Sharma, “Classification of Non-functional Requirements from SRS Documents Using Thematic Roles,” 2016 IEEE International Symposium on Nan electronic and Information Systems (iNIS), Gwalior, 2016, pp. 206-207.
- J. Slankas and L. Williams, "Automated extraction of non-functional requirements in available documentation," 2013 1st International Workshop on Natural Language Analysis in Software Engineering(NaturaLiSE), 2013, pp. 9-16, doi: 10.1109/NAturaLiSE.2013.6611715.
- Chuanyi Li , Liguo Huang , JidongGe , Bin Luo , Vincent Ng , Automatically Classifying User Requests in Crowdsourcing Requirements Engineering, The Journal of Systems & Software (2017), doi: 10.1016/j.jss.2017.12.028.
- Deocadez, R. , Harrison, R. , & Rodriguez, D. (2017). Automatically classifying requirements from app stores: a preliminary study. In Proceedings of the IEEE twenty–fifth international requirements engineering conference workshops September.
- Lu, M., & Liang, P. (2017, June).Automatic Classification of Non-Functional Requirements from Augmented App User Reviews.Proc.21st International Conference on Evaluation and Assessment in Software Engineering, Karlskrona, Sweden.
- Abad, Z. S. H. ,Karras, O. , Ghazi, P. , Glinz, M. , Ruhe, G. , & Schneider, K. (2017). What works better? A study of classifying requirements. In Proceedings of the IEEE twenty-fifth international requirements engineering conference (RE) September.
- Jindal, R. ,Malhotra, R. , & Jain, A. (2016). Automated classification of security requirements. In Proceedings of the 2016 international conference on advances in computing, communications and informatics September.
- Tamai, T., &Anzai, T. (2018). Quality Requirements Analysis with Machine Learning. In ENASE (pp. 241-248).
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